Methods for detection of respiratory effort and sleep apnea monitoring devices

ABSTRACT

A sleep apnea diagnostic system includes a housing that is configured to be attached to near the nose of a patient&#39;s face to sense physiological information of a patient. The housing includes sensors to sense the physiological information. The physiological information may be, for example, air flow through the nose or the mouth or both. The physiological information further may be, for example, blood volume. The sleep apnea diagnostic system includes at least one processor in the housing or external to the housing or both to analyze the physiological information to determine whether the patient has experienced irregular or abnormal respiratory activity and to detect respiratory effort. The analysis may be real time or delayed.

RELATED APPLICATION

This application claims benefit of, and priority under 35 USC §119(e)from U.S. provisional application No. 61/723,682, which is incorporatedby reference herein in its entirety.

FIELD

This invention relates generally to the acquisition of physiologicaldata for health signs monitoring; and, more particularly, for thediagnosis of sleep disorders such as sleep apnea.

BACKGROUND

Obstructive sleep apnea (OSA) is the most common sleep disorder and isresponsible for more mortality and morbidity than any other sleepdisorder. OSA is characterized by recurrent failures to breatheadequately during sleep (termed apneas or hypopneas) as a result ofobstructions in the upper airway.

Apnea is defined as a complete cessation of airflow. Hypopnea is definedas a reduction in airflow disproportionate to the amount of respiratoryeffort expended and insufficient to meet the individual's metabolicneeds. During an apnea or hypopnea, commonly referred to as an abnormalrespiratory event, oxygen levels in the brain decrease, while the carbondioxide levels rise, causing the sleeper to awaken. During an apneicevent, adrenaline and cortisol are released into blood and the heartrate and blood pressure increase. The brief arousals to breathe arefollowed by a return to sleep.

OSA is a serious yet treatable health problem worldwide. Publishedreports indicate that untreated OSA patients are three to five timesmore likely to be involved in industrial and motor vehicle accidents andhave impaired vigilance and memory. Untreated OSA leads to hypertension,stroke, heart failure, irregular heartbeat, heart attack, diabetes anddepression. Current estimates reveal that over 80% of individuals withmoderate to severe OSA remain undiagnosed.

The current standard for the diagnosis of OSA is an expensive overnightsleep study—polysomnography (PSG), which is administered and analyzed bya trained technician and is reviewed by physician specializing in sleepdisorders. A typical overnight PSG includes recording of the followingsignals: electroencephalogram, electromyogram, electrooculogram,respiratory airflow (with oronasal flow monitors), respiratory effort,oxygen saturation (oximetry), electrocardiography, snoring sounds, andbody position. These signals offer a relatively complete collection ofparameters from which respiratory events may be identified and OSA maybe reliably diagnosed.

Obstructive apnea and hypopnea are defined as absence and reduction,respectively, in airflow, in spite of continued effort to breathe, dueto obstruction in the upper airway. Typical polysomnography includessome recording of respiratory effort. The most accurate measure ofeffort is a change in pleural pressure as reflected by an esophagealpressure monitor. Since the esophageal pressure monitoring method isdifficult to administer and highly uncomfortable to patients, othermethods have been developed. These methods estimate respiratory effortand depend on measures of rib cage and abdominal motion and includeinductance or impedance plethysmography, or simple strain gages.

The expense, inconvenience and complexity of traditional PSG sleepstudies have created a significant need for simplified and cheaper OSAdiagnostics. As a result, several portable sleep monitors have beendeveloped over the past several years. These monitors measure fewerparameters than PSG, yet some of them offer the accuracy comparable tothat of PSG. Furthermore, portable OSA monitors bring the convenience ofin-home testing and are often shipped to patients after being prescribedby physician. While significantly less complex than sleep lab PSGequipment, most in-home OSA diagnostic systems require the patient toapply sensors, plug in wires, apply and adjust transducers, straps,gages and other measuring devices, or to operate a computer-controlledbedside unit. This equipment can be difficult for a lay person to applyand properly operate. One of the existing home diagnostic systemsWatch-PAT, manufactured by Itamar Medical Ltd., requires patients towatch an hour-long training presentation prior to using its product.Another example of a difficult to use diagnostic system is AccuSommanufactured by NovaSom. The difficulty that patients experience insetting up in-home devices limits compliance, results in poor quality ofsleep data and limits the adoption of the in-home sleep monitors.Furthermore, the equipment can be uncomfortable and the quality of thesensed data can be poor due to motion artifacts or sensors gettingdisplaced during sleep. One of the main reasons for poor quality ofsleep data is the currently available respiratory effort sensors.Respiratory effort sensors are typically designed as chest or abdominalbands measuring chest expansion and are based on inductiveplethysmography, piezo-electric crystals, conductive elastomeres,magnetometers and strain gauges. These respiratory effort sensors areparticularly prone to motion artifacts and trapping. Occurrence oftrapping artifacts, as a patient turns from one side to another, maysignificantly affect the quality of respiratory effort data. Two studiesfound the failure rate for effort bands ranged between 7% and 21% evenwhen the bands are applied by a trained sleep study technician.

Thus, a device that eliminates or reduces the use of wires, and can bereliably self-applied with minimal instruction would be beneficial toaccurately diagnose patients at risk for OSA. Furthermore, a device thatcan detect respiratory effort without the use of an effort band wouldoffer the convenience and improve the quality of sensed respiratoryeffort data by eliminating trapping artifacts.

There is a known device—ARES—manufactured by Watermark Medical Inc.,which detects respiratory effort by assessing “forehead venous pressure”and is based on an algorithm combining signals from aphotoplethysmography sensor, a pressure sensor and an accelerometer. Thedevice includes nasal tubes for the assessment of nasal airflow and abulky main unit that is attached to the patient's forehead with straps.Due to its form factor and size, the use of airflow tubes and the typesof sensors used, the device is uncomfortable to sleep in, and may beprone to sensor displacement and poor quality of sleep data.

There is also a known device—SleepStrip, which incorporates threethermistors to measure oronasal airflow, a battery, a microcontrollerand a memory in a strip which is applied with adhesive to patient'sface. However, due to the lack of the sensors for oxygen saturation orrespiratory effort, this device is only suitable for screening patientsfor abnormal airflow and is not sufficient for the detection of OSA.While abnormal airflow is a key symptom of OSA, the respiratory effortduring apnea and hypopnea is the physiologic parameter thatdistinguishes OSA from other forms of sleep-disordered breathing such ascentral sleep apnea.

One method proposes the use of photoplethysmography for the detection ofrespiratory effort without the use of effort belts. When analyzing thephotoplethysmography (PPG) signal during an apneic event, it has beensuggested to use a low-pass filter or a frequency analysis to identifyrespiratory induced intensity variation (U.S. Pat. No. 7,690,378).However, these intensity variations are not exclusively due torespiratory effort and therefore, an application of a low-pass filter ora frequency analysis is insufficient to identify respiratory effortduring apneic events. There are several possibilities as to the originof these intensity variations. Inspiration results in a momentaryreduction in stroke volume and, therefore, a corresponding reduction incardiac output, which has an effect on the pulsatile component of thePPG waveform. Also there are blood volume changes during the respiratorycycle due to the transmitted changes in intra-thoracic pressure.Additionally, it has been shown that sympathetically mediatedvasoconstriction of the arteries also plays a part in PPG intensityvariations. It is desired to have a method and system that eliminatesthe role of sympathetically mediated vasoconstriction from PPG intensityvariations during apneic events in order to accurately identifyrespiratory effort.

An easy-to-apply and easy-to-operate diagnostic device, whichincorporates the sensors for accurate detection of respiratory eventsand respiratory effort, is still needed for convenient in-home sleepapnea diagnosis.

SUMMARY

In a general aspect, the present invention relates to a device fordiagnosis of sleep apnea. The device includes a waterproof housingwithin which sensors for airflow, oxygen saturation, heart rate, andrespiratory effort analysis are contained. The housing also includes abattery, a microprocessor, and either an onboard memory to store thedata from the sensors or a wireless data transmission system. Thehousing may contain an adhesive surface to be affixed to patient's skin.

In one embodiment, the device includes a housing shaped to fit thecontours of the patient's face, at least one pair of photosensorspositioned to detect blood volume in the face of the patient, and atleast one air-flow sensor positioned to detect airflow during breathingof the patient. The device further includes a memory disposed in thehousing and configured to store physiological information related to thedetected blood volume and store physiological information related todetected airflow during breathing. The device further includes acontroller disposed in the housing and configured to control at leastone pair of photosensors, to detect blood volume, to control at leastone airflow sensor to detect airflow, and the memory to storephysiological information related to the detected blood volume and storephysiological information related to the detected airflow duringbreathing, during a predefined monitoring period.

In some embodiments, the device and methods analyze a plethysmographysignal (e.g., photoplethysmography) for a slow-moving component (“DCcomponent”) corresponding to changes in blood volume and a fast-movingcomponent (“AC component”) corresponding to arterial pulse waves. The DCcomponent may be used as an estimate of a PPG intensity variation.

In another general aspect, the present invention relates to a computerprogram product comprising of a computer useable medium having computerreadable program code functions embedded in said medium for causing acomputer to acquire and analyze physiological signals reflective ofrespiratory effort.

Implementations of the device may include one or more of the following.The airflow data may be collected with different types of sensors:thermistors or gas flow sensors, for instance. The blood oxygensaturation may be detected with two pairs of photo-emitters andreceptors. The heart rate data may be obtained from thephotoplethysmography (PPG) signal. The patient's position and movementmay be obtained with an accelerometer. The device may include amicrocontroller, a power source and an onboard memory to store collecteddata, or a wireless data transmission system to send the collected data.

Implementations of the method may include one or more of the following.Data on respiratory effort can be obtained indirectly by analyzingchanges in intra-thoracic pressure as evidenced by the changes in thevolume of blood vessels. The trends in the blood volume may be estimatedfrom the PPG signal obtained from peripheral vessels, such as nasalblood vessels.

In one embodiment, a method detects respiratory effort during arespiratory event. The method comprises obtaining oxygen saturation,heart rate and airflow data, analyzing the data to identify arespiratory event, and analyzing the peripheral plethysmography data toidentify respiratory effort during the respiratory event. Therespiratory effort may be identified by detecting changes in the DCcomponent of the plethysmography data during an onset of the respiratoryevent, estimating effect of autonomous nervous system on the DCcomponent of the plethysmography data during the respiratory event, andsubtracting the effect of the autonomous nervous system from theplethysmography data to obtain the respiratory effort.

The described devices and methods provide reliable and accuratedetection of apneic and hypopneic respiratory events. The describeddevices and methods are also simpler, more convenient to use and lessexpensive than other known techniques.

Although the invention has been particularly shown and described withreference to multiple embodiments, it will be understood by personsskilled in the relevant art that various changes in form and details canbe made therein without departing from the spirit and scope of theinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings, which are incorporated in and form a part of thespecification, illustrate embodiments of the present invention and,together with the description, serve to explain the principles of theinvention.

FIG. 1 is a schematic diagram illustrating placement of the sleep apneadiagnostic system in accordance with the present invention.

FIG. 2 a is a schematic diagram illustrating one embodiment and the maincomponents of the diagnostic system of FIG. 1 for the detection of sleepapnea.

FIG. 2 b is a block diagram illustrating one embodiment and the maincomponents of the diagnostic system of FIG. 2 a.

FIG. 3 is a timing diagram illustrating measured signals with oneembodiment of the diagnostic system.

FIG. 4 is a flow chart illustrating effect of an apneic respiratoryeffort on peripheral blood volume.

FIG. 5 is a timing diagram illustrating features of measured signalsduring an apneic episode with respiratory effort.

FIG. 6 is a timing diagram illustrating features of aphotoplethysmography signal during an onset of apneic episode withrespiratory effort.

FIG. 7 is a timing diagram illustrating features of measured signalsduring an apneic episode without respiratory effort.

FIG. 8 is a flow chart illustrating one embodiment of an operation ofthe diagnostic system for the detection of sleep apnea.

FIG. 9 is a flow chart illustrating one embodiment of an operation ofthe diagnostic system for identifying respiratory effort.

FIG. 10 is a timing diagram illustrating features of aphotoplethysmography signal before and during an apneic episode with twopulse waves selected to demonstrate changes in vascular tone as assessedby an augmentation index.

FIGS. 11 a and 11 b are timing diagrams illustrating the analyticalsteps of one embodiment of an operation of the diagnostic system foridentifying respiratory effort in first and second examples of PPGsignals.

DETAILED DESCRIPTION

Various embodiments of the present invention are now described withreference to the figures where like reference numbers indicate identicalor functionally similar elements. Also in the figures, the left mostdigits of each reference number corresponds to the figure in which thereference number is first used.

Reference in the specification to “one embodiment”, “an embodiment”,“various embodiments” or “some embodiments” means that a particularfeature, structure, or characteristic described in connection with theseembodiments is included in at least one embodiment of the invention, andsuch references in various places in the specification are notnecessarily all referring to the same embodiment.

All publications, patents, and patent applications cited herein arehereby incorporated by reference in their entirety for all purposes tothe same extent as if each individual publication, patent, or patentapplication were specifically and individually indicated to be soincorporated by reference.

Methods and apparatus diagnose irregular or abnormal respiratoryactivity of a patient. In various illustrative embodiments, such methodsand apparatus are described below for obstructive sleep apnea, but suchmethods and apparatus may be used in other applications. For example,the methods and apparatus may be used to monitor respiratory activity ofa patient recovering from the effects of anesthesia after surgery inwhich the patient is becoming able to breathe on his own without theassistance of a ventilator as the anesthesia wears off. As anotherexample, the methods and apparatus may be used in critical care units,intensive care units, trauma centers, or emergency rooms to monitor,analyze, and diagnose respiratory activity of patients who, for example,have received head or other bodily trauma, or have overdosed on drugs.As yet another example, the methods and apparatus may be used in medicalfacilities in which the patient has local anesthesia for outpatientsurgery.

A sleep apnea diagnostic system includes a housing that is configured tobe attached to near the nose of a patient's face to sense physiologicalinformation of a patient. The housing includes sensors to sense thephysiological information. The physiological information may be, forexample, air flow through the nose or the mouth or both. Thephysiological information further may be, for example, bloodoxygenation. The sleep apnea diagnostic system includes at least oneprocessor in the housing or external to the housing or both to analyzethe physiological information to determine whether the patient hasexperienced irregular or abnormal respiratory activity. The analysis maybe real time or delayed.

Referring to FIG. 1, an exemplified sleep apnea diagnostic system 100 iscontained inside a flexible housing 101, which is adapted to fit thecontours of the patient's face. The housing 101 includes adhesivesurfaces 102 and 104 disposed on left and right sides, respectively, ofa back surface of the housing 100 that can be affixed to the patient'sskin. The use of the terms “front”, “back”, “left” and “right” are forconvenience and are not to be construed as limiting. In someembodiments, the housing 101 may be affixed to the patient using straps(not shown). The adhesive surfaces 102 and 104 may be selected toprovide sufficient adhesion to the patient under typical sleepconditions for at least a monitoring period (e.g., four hours). In someembodiments, the housing 101 is made of a flexible bio-compatiblepolymer to enhance patient's comfort. In some embodiments, the housing101 is water resistant. In various embodiments, the housing 101 isshaped to fit the skin surface of the patient's nose, upper lip and thearea of maxillary sinuses.

Referring to FIGS. 2 a and 2 b, the diagnostic system 100 includes thehousing 101, comprising the following electronic components. A battery210 may be made of a lithium-polymer or any other material with enoughcapacity to provide power to other electronic components within thehousing 101 for at the monitoring period. A microcontroller 212 operatesother electronic components and stores the sensed physiological data ina memory 214. The microcontroller 212 may be, for example, a controller,a microprocessor or a processor. The memory 214 is sized to store thephysiological data recorded for at least the monitoring period. Thememory 214 may also store program code for the microcontroller 212. Twophoto-emitters 216 and a photo-sensor 218 are positioned on the oppositesides of the patient's nose to collect heart or pulse rate, blood oxygensaturation data, and photoplethysmography data. In some embodiments, thephoto-emitters 216 and the photo-sensor 218 emit and measure reflectionor absorption of light wavelengths typically reflected or absorbed byoxygenated and desoxygenated hemoglobin. Although two photo-emitters 216and a photo-sensor 218 are described, other numbers of photo-emitters216 and photo-sensors 218 may be used. The photo-emitters 216 and thephoto-sensor 218 are coupled to a photoplethysmography transducer 236.The photoplethysmography transducer 236 processes and converts thesensed physiological data from the photo-sensor 218 into a suitable dataformat for the microcontroller 212 and the memory 214. Thephotoplethysmography transducer 236 processes and converts controlsignals from the microcontroller 212 into appropriate signals for thephoto-emitters 216. Two air-flow sensors 220 protrude from the housing101 and are positioned (e.g., near or inside the nostrils of thepatient) to measure nasal airflow. An optional third air-flow sensor(not shown) may be attached to the housing 101 in a similar fashion andpositioned over the patient's mouth to measure oral airflow. In someembodiments, the air-flow sensors 220 are air-pressure sensors ortemperature sensors or a combination of both. The air-flow sensors 220are coupled to an air-flow transducer 238 that processes and convertsthe sensed physiological data from the air-flow sensors 220 into asuitable data format for the microcontroller 212 and the memory 214. Theair-flow sensors 220 may be mounted to a distal end of tubing that isdisposed to extend from the housing 101. The tubing may be detachablefrom the housing 101. In some embodiments, the air-flow transducer 238is an air pressure transducer.

An accelerometer 222 may be included to detect position and motion ofthe patient as an indication of sleep state. A switch 224 may beincluded to allow the patient or a health care worker to initiate and toterminate the sleep data collection. An electrical indicator 226 may beincorporated into the housing 101 to indicate when the system 100 iscollecting data. Interconnection 228 runs completely inside the housing101 and connects the sensors 216, 218, and 220 with other electroniccomponents. In some embodiments, interconnection 228 is a bus. Invarious embodiments, interconnection 228 is wiring. The sensors 216,218, and 220 and the accelerometer 222 may send sensed signals to themicrocontroller 212. The sensed signals may be in analog or digitalform. In some embodiments, the sensors 216, 218 are physically removablefrom the housing 101 and electrically detachable from thephotoplethysmography transducer 236 to thereby be replaceable. In someembodiments, the sensors 220 are physically removable from the housing101 and electrically detachable from the air-flow transducer 238 tothereby be replaceable. The microcontroller 212 may include ananalog-to-digital (A/D) converter for digitizing the sensed signals. Anaudio transducer 230 may monitor the patient to detect snoring or otherrespiratory activity. An internal timer (not shown) is used by themicrocontroller 212 to generate time stamps or time differences or forcontrolling operations.

A computer 234 is coupled via an interface 232 to the interconnection228. In some embodiments, the diagnostic system 100 is placed on apatient to sense physiological information. The diagnostic system 100 isthen later coupled to the computer 234 to download the sensedphysiological information from the memory 214 to the computer 234. Theinterface 232 may include a physical connection to the diagnostic system100 or may be wireless to a wireless system (not shown) in the housing101. The computer 234 performs the data analysis described inconjunction with FIGS. 3-11. In some embodiments, the data analysisdescribed in conjunction with FIGS. 3-11 is performed by themicrocontroller 212 or a combination of the microcontroller 212 and thecomputer 234.

Although the analysis of FIGS. 3-11 is described for a PPG signal, othersignals, such as impedance, volume plethysmography, and tissueplethysmography, may be used.

Referring to FIG. 3, the timing diagram shows collected signal data andcomputed data for the detection of respiratory events. Graph 310 is aphotoplethysmography signal consisting of the fast moving component (“ACcomponent”) reflective of pulse waves sent through the arteries bycontracting heart muscle and the slow moving component (“DC component”)reflective of slower changes in tissue blood volume. Graph 320 is asignal recorded from the thermistors (air-flow sensors 220) andreflective of changes in airflow. Graph 330 represents calculated dataon oxygen saturation. Graph 340 shows changes in heart rate and iscalculated from the photoplethysmography data. The data in graphs 310and 320 was collected simultaneously and shows four respiratory events302. An onset of a respiratory event is characterized by reduction orabsence of tidal airflow and gradual increase in the temperature sensedby the thermistor (for instance, 322), a gradual increase in the DCcomponent of the photoplethysmography signal (for instance, 312), adecrease in oxygen saturation and an increase in heart rate. Thetermination of a respiratory event is characterized by return tobreathing, typically with a first large breath, which is reflected by adecrease in the temperature signal sensed by the thermistor (forinstance, 324) and an increase in light absorption as reflected in theDC component of the photoplethysmography signal (for instance, 314).After the respiratory event is terminated, oxygen saturation recoversand heart rate returns to baseline. Therefore, by monitoring andanalyzing the patterns in the three parameters (airflow data, oxygensaturation level and the calculated heart rate), respiratory events maybe identified. An apneic episode or event can be distinguished from ahypopneic episode by the degree of reduction in airflow. An absence oftidal airflow would indicate an apneic event and a significant reductionin the amplitude of tidal airflow would indicate a hypopneic event.

Referring to FIG. 4, the flow chart illustrates some of the mainphysiological changes that occur during an apneic respiratory event andthe effect of these changes on certain parameters of peripheralphotoplethysmogram. When a person breathes, the pressure inside thechest cavity, called the intra-thoracic pressure, changes with eachbreath. As a person inhales, the chest expands resulting in a decreasein intra-thoracic pressure, which draws air into the lungs. During anexhalation, the intra-thoracic pressure increases and forces air out ofthe lungs. These changes in intra-thoracic pressure also cause changesin the amount of blood returned to the heart via veins and the amount ofblood pumped by the heart into arteries. During an obstructive apneicrespiratory event, the airflow into the lungs is blocked, and anexpansion of rib cage and diaphragm (402) results in a decrease inintra-thoracic pressure (404), which remains lowered due to the lack ofair inflow. The low intra-thoracic pressure causes the central veinslocated in the chest cavity to expand causing an increase in bloodvolume inside the veins. This leads to an overall increase in centralvenous blood volume (406), and a siphoning of venous blood from tissues(408). This also leads to a reduction in cardiac output (410), and areduction in arterial blood flow (412). As a result, the amount of bloodin peripheral blood vessels is temporarily decreased (414). This effecton the peripheral blood volume can be estimated by detecting a temporaryincrease in the slow moving (DC) component of peripheralphotoplethysmogram (416).

These changes in the DC component of peripheral photoplethysmogram canbe reliably assessed only when the photoplethysmography sensor (e.g.,sensors 216 and 218) is positioned over a highly vascularized area fromwhich the venous blood drains into a large vein located close to thevena cava. There are only a few places on the human body where thiscriterion can be met. Nasal positioning of a photoplethysmography sensor(e.g., sensors 216 and 218) is one such location where measurements ofairflow and respiratory effort, by the method described in thisembodiment of the disclosure, can be combined into a single unit devicewithout the use of long tubes or wires. While most of the known sleepapnea diagnostic devices rely on a photoplethysmography signal from adistal location, such approaches result in the PPG signal beingdominated by the effects of the sympathetic nervous system. Therefore,such approaches cannot reliably assess changes in peripheral bloodvolume as a measure of respiratory effort during an apneic episode.Nasal positioning of the PPG sensors (e.g., sensors 216 and 218) resultsin a signal which is influenced by both the respiratory effort and theeffects of the sympathetic nervous system. Such positioning makes itfeasible to distinguish the respiratory effort from the effects of thesympathetic nervous system.

Referring to FIG. 5, the timing diagram shows an example ofphotoplethysmography signal 510 and airflow signal 520 collected duringa time period in which an obstructive apneic event 504 occurred. Airflowdata in graph 520 was collected with a thermistor (air-flow sensor 220)and shows a pre-apneic normal breathing during a time period 502. Eachreduction and a subsequent increase in measured temperature 522correspond to one breath. The onset of the apneic event 504 ischaracterized by a complete cessation of breathing as evidenced by theabsence of tidal airflow. The data shows a post-apneic recovery 506characterized by changes in temperature induced by tidal airflow. A timeperiod 508 shows normal breathing.

Photoplethysmography signal 510 was collected simultaneously with theairflow data and shows a slow-moving (DC) component corresponding tochanges in blood volume and a fast-moving (AC) component correspondingto arterial pulse waves 512. During the onset of the apneic event 504,the DC component gradually increases. During this increase 514, the DCcomponent changes with the frequency similar to the breathing rate ofthe patient. These fluctuations in the DC component are reflective ofrespiratory effort that takes place during the complete cessation ofbreathing. Each respiratory effort 516 alters the DC level such that theminimums of the pulsatile AC component are not aligned with the line 514approximating the overall increase in the DC level.

Referring to FIG. 6, the timing diagram provides an expanded view of thephotoplethysmography signal 510 collected during an onset of the apneicevent 504 described in FIG. 5. In this specific example, eachrespiratory event RE consists of three pulse waves whose minimums followthe same pattern. As the patient expands his rib cage in an attempt todraw air into his lungs, the decrease in DC level (or the minimums ofthe pulse waves) is followed by an increase corresponding to the end ofthe respiratory effort.

Referring to FIG. 7, the timing diagram shows an example ofphotoplethysmography signal 710 and airflow signal 720 collected duringa time period in which a central apneic 704 event occurred. Airflow datain graph 720 was collected with a thermistor (air-flow sensor 220) andshows pre-apneic normal breathing during a time period 702. Eachreduction and a subsequent increase 722 in measured temperaturecorrespond to one breath. The onset of the apneic event 704 ischaracterized by a complete cessation of breathing as evidenced by theabsence of tidal airflow. The data shows a post-apneic recovery 706characterized by tidal airflow induced changes in temperature. A timeperiod 708 shows normal breathing.

Photoplethysmography signal 710 was collected simultaneously with theairflow data and shows a slow moving (DC) component corresponding tochanges in blood volume and a fast moving (AC) component correspondingto arterial pulse waves 712. During the onset of the apneic event 704,the DC component gradually increases. During this increase 714, the DCcomponent does not change with the frequency corresponding to thebreathing rate of the patient. The absence of such fluctuations in theDC component is indicative of the lack of respiratory effort duringapneic episodes in central apnea. The minimums of the pulsatile ACcomponent are aligned with line 714 approximating the overall increasein the DC level.

FIGS. 5, 6 and 7 are examples of physiological data collected frompatients in whom the influence of the sympathetic nervous systems on thePPG DC component is minimal. However, in most patients the effect of thesympathetic nervous system on the PPG DC component is comparable to orlarger than that of respiratory effort. In such patients, the changes inthe PPG DC component are a combination (or superposition) of the twoeffects. As related to the diagnosis of sleep apnea, if the effect ofthe sympathetic nervous system is significant, a patient with a centralsleep apnea will exhibit changes in the PPG DC component that could bemistaken for respiratory effort and an incorrect diagnosis ofobstructive sleep apnea rather than central sleep apnea could be made.Therefore, in order to correctly identify respiratory effort, the effectof the sympathetic nervous system is estimated and subtracted from thePPG DC component.

Referring to FIG. 8, the flow chart illustrates the main steps in usingthe diagnostic system 100 and method for the diagnosis of sleep apnea.At 802, the diagnostic system 100 is applied to the patient and issecured in place. The diagnostic system 100 may be affixed with theadhesive surfaces 102 and 104 to the skin of the patient. The air-flowsensors 220 are positioned near or inside the nostrils of the patient tomeasure nasal airflow. The photo-emitters 216 and the photo-sensor 218are positioned on the opposite sides of the patient's nose to collectblood oxygenation data and photoplethysmography data. Once thediagnostic system 100 is positioned, at 804, the microcontroller 212initiates data collection and continues data collection for at least apredefined time (e.g., for at least four hours) while the patient isasleep. The data from the accelerometer 222 embedded in the air-flowtransducer 238 may aid in identifying the time periods during which thepatient is asleep to ensure that the battery power of the battery 210 isused only when sleep data is collected. In some embodiments, themicrocontroller 212 may collect data before the patient falls asleep.The internal timer (not shown) may be used to identify the time periodsduring which the patient is awake or asleep. At 806, the collected sleepdata may be downloaded from the memory 214 to the computer 234, afterthe patient returns the diagnostic system 100 for analysis ortransmitted while or after the diagnostic system 100 is attached to thepatient. At 808, the computer 234 applies data analysis algorithms tothe collected data to compute oxygen saturation, heart rate and toidentify respiratory events.

At 810, the computer 234 identifies abnormal respiratory events from thesensed physiological data. The identification may be as described abovein conjunction with FIGS. 3 and 5-7 or by other known methods.Additional data analysis algorithms are used to detect respiratoryeffort during apneic respiratory events. These algorithms may includeapplying frequency filters to the photoplethysmography data to identifypresence of DC waves corresponding to respiratory rate. Other approachesmay include a spectral analysis (such as a Fourier analysis) or anyother type of analysis, including visual inspection of the data by atechnician. At 811, the computer 234 identifies respiratory efforts forthe sensed physiological data, such as by the process of FIG. 9. At 812,the computer 234 determines the diagnosis of the respiratory activity,such as obstructive sleep apnea, based on the type, frequency andseverity of respiratory events and the presence of respiratory effortduring apneic events. Diagnosis and scoring of the respiratory eventsmay be automatic or involve manual scoring by a trained technician or aphysician.

FIG. 9 is a flow chart illustrating one embodiment of an operation ofthe diagnostic system for identifying respiratory effort. At 902, thecomputer 234 obtains a PPG signal before and during an apneic event.FIGS. 11 a and 11 b are timing diagrams illustrating the analyticalsteps of one embodiment of an operation of the diagnostic system 100 foridentifying respiratory effort of FIG. 9, and is described inconjunction with FIG. 9. A line 1102 a (FIG. 11 a) is an illustrativeexample of a PPG signal obtained at 902 for a signal in whichrespiratory events are detectable using only low passing filtering. Incontrast, a line 1102 b (FIG. 11 b) is an illustrative example of a PPGsignal obtained at 902 for a signal in which respiratory events are notdetectable using only filtering. Respiratory events occur during aperiod 1120 a (FIGS. 11 a) and 1120 b (FIG. 11 b).

At 904, the computer 234 may filter the PPG signal, for example, using alow pass filter or using a Fourier analysis to obtain PPG DC componentbefore and during the apneic event. A line 1104 a (FIG. 11 a) is anillustrative example of applying at 904 a low pass filter (e.g., with acutoff frequency of 0.3 Hz) to the signal of the line 1102 a. With onlyfiltering, the computer 234 determines, at 904, that a respiratoryeffort occurred. On the other hand, a line 1104 b (FIG. 11 b) is anillustrative example of applying at 904 a low pass filter (e.g., with acutoff frequency of 0.3 Hz) to the signal of the line 1102 b. With onlyfiltering, the computer 234 does not determine, at 904, that arespiratory effort occurred. At 906, the computer 234 estimatesvasoconstriction as a measure of the effect of the autonomous nervoussystem (ANS) on the DC component during the apneic event. At 907, thecomputer 234 computes a parameter describing vascular tone (e.g.,augmentation index). In some embodiments, the computer 234 estimates thepresence and the extent of sympathetically mediated vasoconstriction inthe DC component, for example, by computing the augmentation index foreach of the PPG pulse waves before and during an apneic event. Theaugmentation index is a measure of arterial stiffness and the arteriesbecome stiffer as a result of sympathetically mediated vasoconstriction.Augmentation represents the difference between the second and firstsystolic peaks of the pulse waveform, and the augmentation indexrepresents the augmentation expressed as a percentage of the pulse waveamplitude. The degree of an increase in augmentation index during anapneic event quantifies the effect of vasoconstriction on the DCcomponent during the event. Since heart rate typically increases duringan apneic event, a heart rate normalized measure of augmentation indexyields a better estimate of the effect of vasoconstriction on the DCcomponent of PPG signal. The effect of vasoconstriction on the PPG pulsewaves before and during an apneic event is shown in FIG. 10, which isdescribed in detail below. Other measures of arterial stiffening (suchas pulse wave reflection index, etc.) and changes in vascular tone maybe used to estimate the effect on the DC component. A line 1107 a (FIG.11 a) and a line 1107 b (FIG. 11 b) are illustrative examples ofcomputing, at 907, a parameter using an augmentation index (AI75)normalized to a heart rate of 75 beats per minute. At 908, the computer234 performs a spectral analysis on the timing diagram of the AI75 togenerate frequency intensities. The spectral analysis may be, forexample, a Fourier analysis or a Hilbert analysis. At 909, the computer234 scales the computed parameter based on the intensities of thefrequencies identified in spectral analysis. A line 1109 a (FIG. 11 a)and a line 1109 b (FIG. 11 b) are illustrative examples of scaling, at909, the computed parameter of lines 1107 a and 1107 b, respectively,with the spectral analysis at 909. In some embodiments, the data set ofthe PPG signal and the filtered PPG signal has a dimension that isdifferent from the dimension of the data set of the computed parametersof the PPG pulses. For example, the PPG signal may be sampled at afrequency of 1,000 Hz to give 10,000 data points in a ten second period.The filtered PPG signal may have the same number of data points as thePPG signal. In contrast, the data set of the computer parameters may becorresponding to 10 pulse waves during the ten second period. To adjustthe dimensions of the filtered PPG data and the computed parameters, aninterpolation may be applied such as smoothing or a spline. Also, thePPG signal and the filtered PPG signal may be measured as currentcorresponding to light absorption, while the computed parameter such asaugmentation index, for example, may be computed as percentage. Thecomputer 234 performs processing on the data sets to adjust thedimensions before scaling or after the scaling or both. At 910, once theeffect of vasoconstriction has been estimated, the computer 234subtracts the effect of the autonomous nervous system from the DCcomponent to obtain, at 912, a measure of respiratory effort during theapneic event. A line 1112 a (FIG. 11 a) is an illustrative example ofobtaining, at 912, the respiratory effort during time 1120 a bysubtracting the line 1109 a from the line 1104 a. Although the computer234 did not detect the respiratory effort at 904 in the illustrativeexample of FIG. 11 b, the computer 234 determines the respiratory effortat 912. A line 1112 b (FIG. 11 b) is an illustrative example ofobtaining, at 912, the respiratory effort during time 1120 b bysubtracting the line 1109 b from the line 1104 b.

FIG. 10 is a timing diagram illustrating features of aphotoplethysmography signal 1010 before and during an apneic episodewith two pulse waves 1012 and 1014 selected to demonstrate changes invascular tone as assessed by an augmentation index. A line 1016represents the pulse wave 1012 for which the computer 234 calculates, at902, an augmentation index (AI₇₅) of 6%. A line 1018 represents thepulse wave 1014 for which the computer 234 calculates, at 902, anaugmentation index (AI₇₅) of 20%. The pulse 1012 occurs before theapneic event. The pulse 1018 occurs during the apneic event, and isreflective of more constricted state of the arteries than thatrepresented by the pulse 1012.

As used in the description herein and throughout the claims that follow,“a”, “an”, and “the” includes plural references unless the contextclearly dictates otherwise. Also, as used in the description herein andthroughout the claims that follow, the meaning of “in” includes “in” and“on” unless the context clearly dictates otherwise. Also, as used in thedescription herein and throughout the claims that follow, the meaning of“on” includes “in” and “on” unless the context clearly dictatesotherwise.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps (instructions)leading to a desired result. The steps are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical, magnetic or opticalsignals capable of being stored, transferred, combined, compared andotherwise manipulated. It is convenient at times, principally forreasons of common usage, to refer to these signals as bits, values,elements, symbols, characters, terms, numbers, or the like. Furthermore,it is also convenient at times, to refer to certain arrangements ofsteps requiring physical manipulations of physical quantities as modulesor code devices, without loss of generality.

However, all of these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise as apparentfrom the following discussion, it is appreciated that throughout thedescription, discussions utilizing terms such as “processing” or“computing” or “calculating” or “determining” or “displaying” or thelike, refer to the action and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem memories or registers or other such information storage,transmission or display devices.

Certain aspects of the disclosure include process steps and instructionsdescribed herein in the form of an algorithm. It should be noted thatthe process steps and instructions of the disclosure could be embodiedin software, firmware or hardware, and when embodied in software, couldbe downloaded to reside on and be operated from different platforms usedby a variety of operating systems.

The disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer or in a non-transitory computer-readable media. Such acomputer program may be stored in a computer readable storage medium(e.g., memory 214), such as, but is not limited to, a non-transitoryelectromagnetic medium such as a hard drive, a magnetic disk, a,magnetic-optical disk, an optical disk, a read-only memory (ROM), arandom access memories (RAM), EPROM, EEPROM, magnetic or optical card,application specific integrated circuit (ASIC), a CD-ROM, a DVD,Blu-Ray, a flash memory, a USB memory card, a floppy disk, or any othermedium from which a computer can read. Non-transitory computer-readablemedia comprise all computer-readable media except for a transitory,propagating signal. Furthermore, the computers and microcontrollersreferred to in the specification may include a single processor or maybe architectures employing multiple processor designs for increasedcomputing capability.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may also be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description herein.In addition, the disclosure is not described with reference to anyparticular programming language. It will be appreciated that a varietyof programming languages may be used to implement the teachings of theembodiments as described herein, and any references below to specificlanguages are provided for disclosure of enablement and best mode of theembodiments.

In addition, the language used in the specification has been principallyselected for readability and instructional purposes, and may not havebeen selected to delineate or circumscribe the inventive subject matter.Accordingly, the disclosure is intended to be illustrative, but notlimiting, of the scope of the invention, which is set forth in theclaims.

What is claimed is:
 1. A method for detection of respiratory effort, themethod comprising: obtaining, in a processor, plethysmography data; andidentifying, in the processor, respiratory effort by: detecting changesin a DC component of the plethysmography data, detecting pulse waves inthe plethysmography data, calculating a measure of changes in vasculartone for each detected pulse wave, adjusting dimensions of the measureof the changes in vascular tone to match the detected changes of the DCcomponent of plethysmography data, scaling spectral intensities of themeasure of the changes in vascular tone to match the detected changes ofthe DC component of plethysmography data, and subtracting the adjustedmeasure of the changes in vascular tone from the DC component of theplethysmography data to obtain the respiratory effort.
 2. The method fordetection of respiratory effort according to claim 1, wherein theplethysmography data is obtained during an apneic respiratory event or ahypopneic respiratory event.
 3. The method for detection of respiratoryeffort according to claim 1, wherein plethysmography data is collectedwith a photosensor.
 4. The method for detection of respiratory effortaccording to claim 1, wherein plethysmography data is collected with apressure sensor.
 5. The method for detection of respiratory effortaccording to claim 1, wherein plethysmography data is collected with animpedance sensor.
 6. The method for detection of respiratory effortaccording to claim 1, wherein detecting changes in a DC component of theplethysmography data includes applying a frequency filter to theplethysmography data.
 7. The method for detection of respiratory effortaccording to claim 1, wherein adjusting dimensions of the measure of thechanges in vascular tone includes a spectral analysis.
 8. The method fordetection of respiratory effort according to claim 1, wherein theplethysmography data is obtained from a plethysmography sensor that ispositioned over a highly vascularized area from which the venous blooddrains into a large vein located near a vena cava of a person.
 9. Amethod for detection of respiratory effort, the method comprising:obtaining, in a processor, plethysmography data; and identifying, in theprocessor, respiratory effort by: detecting changes in a DC component ofthe plethysmography data, detecting pulse waves in the plethysmographydata, calculating a measure of changes in vascular tone for eachdetected pulse wave, via an augmentation index determined by thedifference between first and second systolic peaks of the pulsewaveform, adjusting dimensions of the measure of the changes in vasculartone to match the detected changes of the DC component ofplethysmography data, and subtracting the adjusted measure of thechanges in vascular tone from the DC component of the plethysmographydata to obtain the respiratory effort.